Book Image

Learning R Programming

By : Kun Ren
Book Image

Learning R Programming

By: Kun Ren

Overview of this book

R is a high-level functional language and one of the must-know tools for data science and statistics. Powerful but complex, R can be challenging for beginners and those unfamiliar with its unique behaviors. Learning R Programming is the solution - an easy and practical way to learn R and develop a broad and consistent understanding of the language. Through hands-on examples you'll discover powerful R tools, and R best practices that will give you a deeper understanding of working with data. You'll get to grips with R's data structures and data processing techniques, as well as the most popular R packages to boost your productivity from the offset. Start with the basics of R, then dive deep into the programming techniques and paradigms to make your R code excel. Advance quickly to a deeper understanding of R's behavior as you learn common tasks including data analysis, databases, web scraping, high performance computing, and writing documents. By the end of the book, you'll be a confident R programmer adept at solving problems with the right techniques.
Table of Contents (21 chapters)
Learning R Programming
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface

Introducing object-oriented programming


If you are a developer from programming languages such as Java, Python, C++, C#, you should feel familiar with the object-oriented style of coding. However, if you are not familiar with any other object-oriented programming languages, you will probably be puzzled by this term, as it sounds a bit abstract. However, don't worry; this is much easier to understand than it looks if we think about the core of programming.

When we talk about programming, we are actually talking about using programming tools to solve problems. Before solving the problem, we need to model the problem first. Traditionally, we usually figured out an algorithm that takes several steps to solve a numeric computing problem. Then, we wrote some procedural code to implement the algorithm. For example, most statistical algorithms are implemented in a procedural style, that is, by transforming the input into the output according to the theory, step by step.

However, many problems are...